Online bad data detection using kernel density estimation

Muhammad Sharif Uddin, Anthony Kuh, Yang Weng, Marija Ilić

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies the bad data. The algorithm was then tested on the IEEE 14-bus test system where it demonstrated superior performance detecting critical and multiple bad data compared to the largest normalized residual test.

Original languageEnglish (US)
Title of host publication2015 IEEE Power and Energy Society General Meeting, PESGM 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467380409
StatePublished - Sep 30 2015
Externally publishedYes
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States
Duration: Jul 26 2015Jul 30 2015


OtherIEEE Power and Energy Society General Meeting, PESGM 2015
Country/TerritoryUnited States


  • bad data detection
  • density estimation
  • online algorithm
  • smart grids

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering


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